This paper addresses the issue of comparability of comments extracted from Youtube. The comments concern spoken Algerian that could be either local Arabic, Modern Standard Arabic or French. This diversity of expression gives rise to a huge number of problems concerning the data processing. In this article, several methods of alignment will be proposed and tested. The method which permits to best align is Word2Vecbased approach that will be used iteratively. This recurrent call of Word2Vec allows us improve significantly the results of comparability. In fact, a dictionary-based approach leads to a Recall of 4, while our approach allows one to get a Recall of 33 at rank 1. Thanks to this approach, we built from Youtube CALYOU, a Comparable Corpus of the spoken Algerian.
Code-switching (CS) is the phenomenon that occurs when a speaker alternates between two or more languages within an utterance or discourse. In this work, we investigate the existence of code-switching in formal text, namely proceedings of multilingual institutions. Our study is carried out on the Arabic-English code-mixing in a parallel corpus extracted from official documents of United Nations. We build a parallel code-switched corpus with two reference translations one in pure Arabic and the other in pure English. We also carry out a human evaluation of this resource in the aim to use it to evaluate the translation of code-switched documents. To the best of our knowledge, this kind of corpora does not exist. The one we propose is unique. This paper examines several methods to translate codeswitched corpus: conventional statistical machine translation, the end-to-end neural machine translation and multitask-learning.
Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right hamoza above or below Alif.
In this paper we present the results of the integration works on the system designed for automated summarization and translation of newscast and reports. We show the proposed system architectures and list the available software modules. Thanks to well defined interfaces the software modules may be used as building blocks allowing easy experimentation with different summarization scenarios.
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